In this TACC Podcast, Chemists at the University of California, San Diego describe how they used supercomputing to design a sheet of proteins that toggle between different states of porosity and density. This is a first in biomolecular design that combined experimental studies with computation done on supercomputers. “To meet these and other computational challenges, Paesani has been awarded supercomputer allocations through XSEDE, the Extreme Science and Engineering Discovery Environment, funded by the National Science Foundation.”
Designing Interfaces in Materials with Supercomputers
April 29, 2016 by Leave a Comment
Designing materials atom-by-atom has long been a science fiction dream. Georg Schusteritsch and Chris Pickard of the University of Cambridge are bringing science fiction one step closer to reality using the UK National Supercomputing Facility, ARCHER to reveal the interfaces forming within and between materials. “We have developed a general first-principles approach to predict the crystal structure of interfaces in materials, a technique that represents a major step towards computationally developing materials with specially designed interfaces.”
Materials Discovery by Computation – a Revolution Still in the Making
June 25, 2015 by Leave a Comment
“We will describe recent progress and successes obtained in predicting properties of matter by quantum simulations, and discuss algorithmic challenges in connection with the use of evolving high-performance computing architectures. We will also discuss open issues related to the validation of the approximate, first principles theories used in large-scale quantum simulations.”
New Algorithm for Real-Time Simulations in Materials Research
February 21, 2015 by Leave a Comment
Researchers at LBNL have have developed a new algorithm that opens the door for real-time simulations in atomic-level materials research. “By eliminating higher energy terms, you significantly reduce the dimension of your problem, and you can also use a bigger time step,” explained Wang, describing the key to the algorithm’s success: Solving the equations in bigger time steps reduces the computational cost and increases the speed of the simulations.
